Reading and Writing Spatial Data

Reading Files

Use geopandas.read_file() to import vector spatial data:

import geopandas as gpd

# Read from file
gdf = gpd.read_file("data.shp")
gdf = gpd.read_file("data.geojson")
gdf = gpd.read_file("data.gpkg")

# Read from URL
gdf = gpd.read_file("https://example.com/data.geojson")

# Read from ZIP archive
gdf = gpd.read_file("data.zip")

Performance: Arrow Acceleration

For 2-4x faster reading, use Arrow:

gdf = gpd.read_file("data.gpkg", use_arrow=True)

Requires PyArrow: uv pip install pyarrow

Filtering During Read

Pre-filter data to load only what's needed:

# Load specific rows
gdf = gpd.read_file("data.gpkg", rows=100)  # First 100 rows
gdf = gpd.read_file("data.gpkg", rows=slice(10, 20))  # Rows 10-20

# Load specific columns
gdf = gpd.read_file("data.gpkg", columns=['name', 'population'])

# Spatial filter with bounding box
gdf = gpd.read_file("data.gpkg", bbox=(xmin, ymin, xmax, ymax))

# Spatial filter with geometry mask
gdf = gpd.read_file("data.gpkg", mask=polygon_geometry)

# SQL WHERE clause (requires Fiona 1.9+ or Pyogrio)
gdf = gpd.read_file("data.gpkg", where="population > 1000000")

# Skip geometry (returns pandas DataFrame)
df = gpd.read_file("data.gpkg", ignore_geometry=True)

Writing Files

Use to_file() to export:

# Write to Shapefile
gdf.to_file("output.shp")

# Write to GeoJSON
gdf.to_file("output.geojson", driver='GeoJSON')

# Write to GeoPackage (supports multiple layers)
gdf.to_file("output.gpkg", layer='layer1', driver="GPKG")

# Arrow acceleration for faster writing
gdf.to_file("output.gpkg", use_arrow=True)

Supported Formats

List all available drivers:

import pyogrio
pyogrio.list_drivers()

Common formats: Shapefile, GeoJSON, GeoPackage (GPKG), KML, MapInfo File, CSV (with WKT geometry)

Parquet and Feather

Columnar formats preserving spatial information with support for multiple geometry columns:

# Write
gdf.to_parquet("data.parquet")
gdf.to_feather("data.feather")

# Read
gdf = gpd.read_parquet("data.parquet")
gdf = gpd.read_feather("data.feather")

Advantages:

Faster I/O than traditional formats
Better compression
Preserves multiple geometry columns
Schema versioning support

PostGIS Databases

Reading from PostGIS

from sqlalchemy import create_engine

engine = create_engine('postgresql://user:password@host:port/database')

# Read entire table
gdf = gpd.read_postgis("SELECT * FROM table_name", con=engine, geom_col='geometry')

# Read with SQL query
gdf = gpd.read_postgis("SELECT * FROM table WHERE population > 100000", con=engine, geom_col='geometry')

Writing to PostGIS

# Create or replace table
gdf.to_postgis("table_name", con=engine, if_exists='replace')

# Append to existing table
gdf.to_postgis("table_name", con=engine, if_exists='append')

# Fail if table exists
gdf.to_postgis("table_name", con=engine, if_exists='fail')

Requires: uv pip install psycopg2 or uv pip install psycopg and uv pip install geoalchemy2

File-like Objects

Read from file handles or in-memory buffers:

# From file handle
with open('data.geojson', 'r') as f:
    gdf = gpd.read_file(f)

# From StringIO
from io import StringIO
geojson_string = '{"type": "FeatureCollection", ...}'
gdf = gpd.read_file(StringIO(geojson_string))

Remote Storage (fsspec)

Access data from cloud storage:

# S3
gdf = gpd.read_file("s3://bucket/data.gpkg")

# Azure Blob Storage
gdf = gpd.read_file("az://container/data.gpkg")

# HTTP/HTTPS
gdf = gpd.read_file("https://example.com/data.geojson")

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